Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: In this paper, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources.
223 citations
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TL;DR: A convergence proof based on the finiteness of the set of distinct cut coefficients is provided, which differs from existing published proofs in that it does not require a restrictive assumption.
223 citations
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TL;DR: A stochastic dynamic programming (DP) model of the fashion buying problem that incorporates the model of demand and an updated Newsboy heuristic that is intuitively appealing and easily implemented are developed.
Abstract: We focus on the problem of buying fashion goods for the “big book” of a catalogue merchandiser. This company also owns outlet stores and thus has the opportunity, as the season evolves, to divert inventory originally purchased for the big book to the outlet store. The obvious questions are: (1) how much to order originally, and (2) how much to divert to the outlet store as actual demand is observed. We develop a model of demand for an individual item. The model is motivated by data from the women's designer fashion department and uses both historical data and buyer judgement. We build a stochastic dynamic programming (DP) model of the fashion buying problem that incorporates the model of demand. The DP model is used to derive the structure of the optimal inventory control policy. We then develop an updated Newsboy heuristic that is intuitively appealing and easily implemented. When this heuristic is compared to the optimal solution for a wide variety of scenarios, we observe that it performs very well. Si...
222 citations
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TL;DR: In this article, two alternative methodologies to efficiently generate electric load and wind-power production scenarios, which are used as input data for investment problems, are proposed. But, they do not consider the impact of wind power investment on the overall system.
222 citations
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TL;DR: The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties.
Abstract: A bicriterion, multiperiod, stochastic mixed-integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value-at-risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L-shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers AIChE J, 2012
222 citations